Chitosan-Based Electronics: The Importance of Acid Strength and Plasticizing Additives on Device Performance
Bibliographic record
Abstract
A rise in demand for disposable consumer electronics such as smart packaging, wearable electronics, and single-use point-of-source sensors requires the development of eco-friendly and compostable electronic materials. Chitosan is derived from crustacean waste and offers high dielectric constant values without requiring rigorous purification, making it sustainable for large-scale electronic device manufacturing. When processed in acidic media, the protonated backbone of chitosan pairs with counterions from the acid dissociation to form chitosan thin films with electrical double layers (EDLs) and tunable capacitive properties. We report the importance of the choice of acid when processing chitosan by surveying a series of halogenated and biosourced acids with varying p K a values and solutions with different pH values. Oxalic acid outperforms other acids, with a maximum areal capacitance of 161 nF·mm –2 . Tartaric acid and citric acid, despite lower capacitance values, showed promising results with a stable EDL capacitance and high reproducibility, making them optimal for large-area manufacturing. The incorporation of sorbitol as a plasticizer boosts the EDL formation onset of all chitosan–acid combinations to 1 × 10 3 –10 5 Hz and improves reproducibility. High-performing single-walled carbon nanotube thin film transistors were made using chitosan-based dielectrics treated with different acids with and without sorbitol, leading to transconductance as high as ≈5.2 μS and I on / I off of 10 5 . The capacitors and transistors remain functional after one year of storage in ambient conditions. Overall, this study demonstrates durable high-performance electronics based on chitosan and stresses the importance of processing acid and the use of plasticizing additives, such as sorbitol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".